Comparative analysis of different algorithms for VAS station land cover classification with limited training points
The Valencian Anchor Station (VAS) (Spain) is an outstanding site operating as a central location for calibrating and validating numerous remote sensing instruments and products. Hence, an accurate characterization of its land cover is required. This research conducts a land cover classification wit...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001840 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850135597978484736 |
|---|---|
| author | D. García-Rodríguez A. Pérez-Hoyos B. Martínez Pablo Catret Ruber J. Javier Samper-Zapater E. López-Baeza J.J. Martínez Durá |
| author_facet | D. García-Rodríguez A. Pérez-Hoyos B. Martínez Pablo Catret Ruber J. Javier Samper-Zapater E. López-Baeza J.J. Martínez Durá |
| author_sort | D. García-Rodríguez |
| collection | DOAJ |
| description | The Valencian Anchor Station (VAS) (Spain) is an outstanding site operating as a central location for calibrating and validating numerous remote sensing instruments and products. Hence, an accurate characterization of its land cover is required. This research conducts a land cover classification within the VAS station (10 × 10 km2) and its surrounding area (30 × 30 km2) for 2021 using multi-temporal imagery from Sentinel-2 Multispectral Instrument (MSI). Several aspects of land cover classification have been evaluated, including i) the feature selection, ii) the temporal resolution of time series (i.e., monthly, seasonal), iii) the performance of six Machine Learning algorithms (i.e., CART, GTB, k-NN, NB, RF, and SVM, alongside three deep learning models (FC-NN, MLP-ED, and ResCNN) and iv) the optimization of classifier tuning parameters. Furthermore, the study assesses the impact of reducing sample sizes on classifying similar areas, extending the classification to three buffers (1 km, 5 km and 10 km) without increasing reference data. ResCNN emerged as the best-performing model, yielding superior classification metrics (96 % overall accuracy and 95 % kappa score) in July, coinciding with the peak vineyard phenology. Producer’s and user’s accuracy values generally exceeded 90 % for most land cover classes, with some exceptions in more challenging categories such as artificial surfaces and non-irrigated arable land, which showed lower accuracies due to inter-class similarity. Overall, the findings underscore the robust performance of all models in land cover classification, demonstrating the feasibility of achieving high-quality classification with a robust methodology and limited training data. |
| format | Article |
| id | doaj-art-e474880a04a0481082afd7a66d55c699 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-e474880a04a0481082afd7a66d55c6992025-08-20T02:31:22ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910453710.1016/j.jag.2025.104537Comparative analysis of different algorithms for VAS station land cover classification with limited training pointsD. García-Rodríguez0A. Pérez-Hoyos1B. Martínez2Pablo Catret Ruber3J. Javier Samper-Zapater4E. López-Baeza5J.J. Martínez Durá6University Research Institute on Robotics and Information and Communication Technologies (IRTIC), Universitat de València, Paterna 46980, Spain; Corresponding author.Albavalor, Science Park Universitat de València, Paterna 46980, SpainEnvironmental Remote Sensing Group (UV-ERS), Universitat de València, 46100 València, SpainUniversity Research Institute on Robotics and Information and Communication Technologies (IRTIC), Universitat de València, Paterna 46980, SpainUniversity Research Institute on Robotics and Information and Communication Technologies (IRTIC), Universitat de València, Paterna 46980, SpainEnvironmental Remote Sensing Group (UV-ERS), Universitat de València, 46100 València, SpainUniversity Research Institute on Robotics and Information and Communication Technologies (IRTIC), Universitat de València, Paterna 46980, SpainThe Valencian Anchor Station (VAS) (Spain) is an outstanding site operating as a central location for calibrating and validating numerous remote sensing instruments and products. Hence, an accurate characterization of its land cover is required. This research conducts a land cover classification within the VAS station (10 × 10 km2) and its surrounding area (30 × 30 km2) for 2021 using multi-temporal imagery from Sentinel-2 Multispectral Instrument (MSI). Several aspects of land cover classification have been evaluated, including i) the feature selection, ii) the temporal resolution of time series (i.e., monthly, seasonal), iii) the performance of six Machine Learning algorithms (i.e., CART, GTB, k-NN, NB, RF, and SVM, alongside three deep learning models (FC-NN, MLP-ED, and ResCNN) and iv) the optimization of classifier tuning parameters. Furthermore, the study assesses the impact of reducing sample sizes on classifying similar areas, extending the classification to three buffers (1 km, 5 km and 10 km) without increasing reference data. ResCNN emerged as the best-performing model, yielding superior classification metrics (96 % overall accuracy and 95 % kappa score) in July, coinciding with the peak vineyard phenology. Producer’s and user’s accuracy values generally exceeded 90 % for most land cover classes, with some exceptions in more challenging categories such as artificial surfaces and non-irrigated arable land, which showed lower accuracies due to inter-class similarity. Overall, the findings underscore the robust performance of all models in land cover classification, demonstrating the feasibility of achieving high-quality classification with a robust methodology and limited training data.http://www.sciencedirect.com/science/article/pii/S1569843225001840Land-cover classificationFeature selectionMachine learningDeep learningMulti-temporal training set size |
| spellingShingle | D. García-Rodríguez A. Pérez-Hoyos B. Martínez Pablo Catret Ruber J. Javier Samper-Zapater E. López-Baeza J.J. Martínez Durá Comparative analysis of different algorithms for VAS station land cover classification with limited training points International Journal of Applied Earth Observations and Geoinformation Land-cover classification Feature selection Machine learning Deep learning Multi-temporal training set size |
| title | Comparative analysis of different algorithms for VAS station land cover classification with limited training points |
| title_full | Comparative analysis of different algorithms for VAS station land cover classification with limited training points |
| title_fullStr | Comparative analysis of different algorithms for VAS station land cover classification with limited training points |
| title_full_unstemmed | Comparative analysis of different algorithms for VAS station land cover classification with limited training points |
| title_short | Comparative analysis of different algorithms for VAS station land cover classification with limited training points |
| title_sort | comparative analysis of different algorithms for vas station land cover classification with limited training points |
| topic | Land-cover classification Feature selection Machine learning Deep learning Multi-temporal training set size |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001840 |
| work_keys_str_mv | AT dgarciarodriguez comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints AT aperezhoyos comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints AT bmartinez comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints AT pablocatretruber comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints AT jjaviersamperzapater comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints AT elopezbaeza comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints AT jjmartinezdura comparativeanalysisofdifferentalgorithmsforvasstationlandcoverclassificationwithlimitedtrainingpoints |